index – Nicholas Giangreco

This is where I till my projects and collect my musings

🌳Welcome to my digital garden🌳

A digital garden is an online space for sharing and cultivating knowledge 🌈 It embodies the notion of working out loud 🤓

Projects ➡️ Collection of code, notebooks, packages, and/or writings

Posts ➡️ Announcements, standalone essay, or opinions

Discussion ➡️ Github issues

Projects
Posts

Buffalo & Western New York

Exploring my hometown through data

Monte Carlo Cross Validation

Clinical machine learning framework

Pediatric Drug Safety

Research, data, and analyses on reported adverse medication outcomes across childood

Showcasing NFL NextGenStats

Project for learning a new data domain and new programming techniques, including shiny modules.

Diagnostics in ggplot2 plots

Adding diagnostics to plots for validating the data plotted

Deep learning fail - it happens

I failed at training a deep learning model - this post sums it up. In the end, its ok because though my model didn’t, I learned!

New learning journey: Deep Learning with R

Starting to read about apply DL algorithms in R, converting between R and Python

The end of the beginning to a NFL player performance dashboard

A prototype for my new dashboard hobby project, and why all prototypes have an ending

A dashboard for interacting with NFL data

The start of a dashboard, hobby project to understand more about sports data

Object Oriented Programming (OOP) with Datasets

Showing the idea of using OOP (i.e. S3) to define and manipulate datasets in a standard way

Shinylive - shiny apps with no server

Minimal example showing an interactive webpage in shiny without a server!

What Baby Names are In and Out of Fashion?

Using open data to summarize baby names over time

Identifying Open Data about the City of Buffalo and Western New York

Starting the journey to make some interesting and fun insights about my hometown by identifying open datasets

Announcing mccvshiny

mccvshiny is a Shiny for Python app that showcases the flexibility of the peer-reviewed Monte Carlo Cross Validation (MCCV) algorithm using simulated data distributions

Announcing the kidsides R package

We hypothesized that adverse drug reactions manifest from the interaction between drug exposure and dynamic biological processes during child growth and development. The {kidsides} R data package contains half a million drug safety hypotheses.

Announcing the mccv python package with Quarto website

Monte Carlo Cross Validation (MCCV) is a peer-reviewed clinical machine learning algorithm that is now implemented in a package and showcased in a website built by Quarto

Explorations in Pediatric Drug Safety

montelukast-associated systematic disorders

Hello, world!

First post! Starting and describing my digital space.